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应用生态学报 ›› 2003, Vol. ›› Issue (8): 1291-1295.

• 研究论文 • 上一篇    下一篇

不同空间尺度下的ALMANAC模型验证

谢云1, James Kiniry1, 刘宝元2   

  1. 1. 北京师范大学资源与环境科学系, 环境演变与自然灾害教育部重点实验室, 北京 100875;
    2. 美国农业部草原、水土研究实验室, 得州 76502
  • 收稿日期:2001-05-31 修回日期:2001-11-26 出版日期:2003-08-15
  • 通讯作者: 谢云,女,1964年生,博士,副教授,主要从事气候及其影响评价、作物生长模型等研究,发表论文30余篇.E-mail:xieyun@bnu.edu.cn.
  • 基金资助:
    国家重点基础研究发展规划项目(G2000018605);国家教育部留学回国人员科研启动基金;中国科学院禹城农业综合生态实验站资助项目

Validation of the ALMANAC model with different spatial scale

XIE Yun1, James Kiniry1, LIU Baoyuan2   

  1. 1. Department of Resource and Environmental Sciences, Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education of China, Beijing Normal University, Beijing 100875, China;
    2. USDA/Agricultural Research Service/Grassland, Soil and Water Research Laboratory, TX76502, USA
  • Received:2001-05-31 Revised:2001-11-26 Online:2003-08-15

摘要: ALMANAC模型最早作为EPIC模型的一部分,用于模拟土壤侵蚀导致的土地生产力的下降.它将试验数据的统计过程和作物生长的机理过程结合起来,是一种典型的基于过程模拟的应用型作物生长模型.如能在不同的空间尺度上验证模型的适用性,无疑会大大扩展模型的应用范围.从这一目的出发,利用美国得克萨斯州19个试验田和9个县的玉米和高粱产量资料及其相关的作物、土壤、田间管理等数据,模拟了1998年田间尺度,1989~1998年县级尺度的平均作物产量.模拟结果表明,ALMANAC模型能够很好地模拟两种不同空间尺度的作物产量,其相对误差在田间尺度上分别为8.9%(高粱)和9.4%(玉米),在县级尺度上分别达到2.6%(玉米)和-0.6%(高粱).该模型在进行产量预测、掌握作物生长动态,指导农业生产管理和土地利用等方面具有很好的应用前景.

Abstract: The ALMANACmodel was validated in a drought stressed year and in a period from 1989 to 1998 at different sites of Texas to evaluate its ability in simulating maize and sorghum yields at different spatial scales and to extend its application range. There were 11 sites for maize and 8 sites for sorghum in plot size simulations, and 9 counties for maize and sorghum in county level simulations. The model showed similar accuracy in simulating both plot size and county level mean grain yields. It could also simulate single year yields under water limited climatic conditions for several sites and mean county yields of maize and sorghum, and had small CVvalues of mean yields for a long term prediction. The mean error was 8.9% for sorghum and 9.4% for maize in field scale simulations, and was only 2.6% for maize and 0.6% for sorghum in county level mean yield simulations.Crop models often require extensive input data sets to realistically simulate crop growth. The development of such input data sets is difficult for some model users. The soil, weather, and crop parameter data sets developed in this study could be used as the guidelines for model applications in similar climatic regions and on similar soils.

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